| 研究開始時の研究の概要 |
This research will focus on driver-automation mutual adaptation with haptic interface. First, I will test the hypothesis that the driver-automation mutual adaptation could be modeled using data-driven approach based on driver’s integration of visual and haptic sensory information. Second, it is expected that drivers would feel the mutually adaptive system more trustworthy and would be more satisfied compared to one-way adaptation. Third, driver-automation cooperative performance, including reduced driver workload and lane departure risks, will be improved through mutual adaptation.
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| 研究実績の概要 |
Our research focuses on driver-automation mutual adaptation for haptic shared control.
In FY 2023, our research achievements are summarized as follows:
1. A driver model was developed to capture lateral control behavior during driver-vehicle shared control, integrating a gated recurrent unit network for evaluation against state-of-the-art models. The analysis highlights the network's superior accuracy in predicting driver behavior and maintaining acceptable lateral position error, demonstrating robustness in driver-automation shared control systems
2. Based on previous experimental results showing improved lane-keeping performance and collaborative behaviors with shared control strategies, a novel driving simulator study is designed to test a mutual adaptive shared control system with updating trust values. The study verifies the effectiveness of the proposed system through interactive simulation, focusing on improvements in lane-keeping performance, collaborative behaviors, and user satisfaction.
3. A high-fidelity driving simulator experiment was conducted to present a comprehensive comparison between the Personalized Predictive Haptic Steering Assistance System and a Manually Triggered System, evaluating their effectiveness in lane-keeping and lane-changing assistance with different haptic torque strengths. The results showed that both systems reduced lane departure risk and shortened lane-changing time, but they differed in their impact on driver focus, with the Predictive-Strong method maintaining attention better than the Triggered-Strong system.
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